Data-sets in such contexts are thus composed of attributed time-series data. Several effective first-line approaches exist for time-series forecasting; for instance, ARIMA, SARIMA, Holt-Winters [1]} etc. are time-tested methods. ARIMA and its variants cannot natively handle attributed time-series data. For regression problems (including forecasting) that involve attributed data, Gradient Boosting (GB) variants (e.g., XGBoost [2]}) and Random Forests (RF) are well-established approaches. The former are particularly effective due to power of additive modeling. Most classical time-series forecasting methods are statistical approaches, that make use of the statistical (e.g., linear) relationship between predictors and target variable whereas GB, RF and other such partition-based approaches are piece-wise constant in the leaves of different stubs (base estimators).
Our proposed approach combines the best from both these approaches. Tree-based approaches are used to effectively partition attributed time-series data and at the leaves, time-series regression models are employed to leverage patterns in the data. Standard ML libraries (e.g. Scikit-Learn https://scikit-learn.org/) do not offer statistically-enhanced data-partitioned regression algorithms currently.
